Update webui/run.py

webui/run.py

@@ -1,89 +1,862 @@
-#!/usr/bin/env python3
-"""
-Kronos Web UI startup script
-"""
-
-import os
-import sys
-import subprocess
-import webbrowser
-import time
-
-def check_dependencies():
-    """Check if dependencies are installed"""
-    try:
-        import flask
-        import flask_cors
-        import pandas
-        import numpy
-        import plotly
-        print("✅ All dependencies installed")
-        return True
-    except ImportError as e:
-        print(f"❌ Missing dependency: {e}")
-        print("Please run: pip install -r requirements.txt")
-        return False
-
-def install_dependencies():
-    """Install dependencies"""
-    print("Installing dependencies...")
-    try:
-        subprocess.check_call([sys.executable, "-m", "pip", "install", "-r", "requirements.txt"])
-        print("✅ Dependencies installation completed")
-        return True
-    except subprocess.CalledProcessError:
-        print("❌ Dependencies installation failed")
-        return False
-
-def main():
-    """Main function"""
-    print("🚀 Starting Kronos Web UI...")
-    print("=" * 50)
-    
-    # Check dependencies
-    if not check_dependencies():
-        print("\nAuto-install dependencies? (y/n): ", end="")
-        if input().lower() == 'y':
-            if not install_dependencies():
-                return
-        else:
-            print("Please manually install dependencies and retry")
-            return
-    
-    # Check model availability
-    try:
-        sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-        from model import Kronos, KronosTokenizer, KronosPredictor
-        print("✅ Kronos model library available")
-        model_available = True
-    except ImportError:
-        print("⚠️  Kronos model library not available, will use simulated prediction")
-        model_available = False
-    
-    # Start Flask application
-    print("\n🌐 Starting Web server...")
-    
-    # Set environment variables
-    os.environ['FLASK_APP'] = 'app.py'
-    os.environ['FLASK_ENV'] = 'development'
-    
-    # Start server
-    try:
-        from app import app
-        print("✅ Web server started successfully!")
-        print(f"🌐 Access URL: http://localhost:7070")
-        print("💡 Tip: Press Ctrl+C to stop server")
-        
-        # Auto-open browser
-        time.sleep(2)
-        webbrowser.open('http://localhost:7070')
-        
-        # Start Flask application
-        app.run(debug=True, host='0.0.0.0', port=7070)
-        
-    except Exception as e:
-        print(f"❌ Startup failed: {e}")
-        print("Please check if port 7070 is occupied")
-
-if __name__ == "__main__":
-    main()
+import datetime
+import json
+import os
+import sys
+import warnings
+
+import pandas as pd
+import plotly.graph_objects as go
+import plotly.utils
+import pytz
+from binance.client import Client
+from flask import Flask, render_template, request, jsonify
+from flask_cors import CORS
+from sympy import false
+
+try:
+    from technical_indicators import add_technical_indicators, get_available_indicators
+
+    TECHNICAL_INDICATORS_AVAILABLE = False
+except ImportError as e:
+    print(f"⚠️ 技术指标模块导入失败: {e}")
+    TECHNICAL_INDICATORS_AVAILABLE = False
+
+
+    # 定义空的替代函数
+    def add_technical_indicators(df, indicators_config=None):
+        return df
+
+
+    def get_available_indicators():
+        return {'trend': [], 'momentum': [], 'volatility': [], 'volume': []}
+
+warnings.filterwarnings('ignore')
+
+# 设置东八区时区
+BEIJING_TZ = pytz.timezone('Asia/Shanghai')
+
+# Add project root directory to path
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+try:
+    from model import Kronos, KronosTokenizer, KronosPredictor
+
+    MODEL_AVAILABLE = True
+except ImportError:
+    MODEL_AVAILABLE = False
+    print("Warning: Kronos model cannot be imported, will use simulated data for demonstration")
+
+app = Flask(__name__)
+CORS(app)
+
+# Global variables to store models
+tokenizer = None
+model = None
+predictor = None
+
+# Available model configurations
+AVAILABLE_MODELS = {
+    'kronos-mini': {
+        'name': 'Kronos-mini',
+        'model_id': 'NeoQuasar/Kronos-mini',
+        'tokenizer_id': 'NeoQuasar/Kronos-Tokenizer-2k',
+        'context_length': 2048,
+        'params': '4.1M',
+        'description': 'Lightweight model, suitable for fast prediction'
+    },
+    'kronos-small': {
+        'name': 'Kronos-small',
+        'model_id': 'NeoQuasar/Kronos-small',
+        'tokenizer_id': 'NeoQuasar/Kronos-Tokenizer-base',
+        'context_length': 512,
+        'params': '24.7M',
+        'description': 'Small model, balanced performance and speed'
+    },
+    'kronos-base': {
+        'name': 'Kronos-base',
+        'model_id': 'NeoQuasar/Kronos-base',
+        'tokenizer_id': 'NeoQuasar/Kronos-Tokenizer-base',
+        'context_length': 512,
+        'params': '102.3M',
+        'description': 'Base model, provides better prediction quality'
+    }
+}
+
+
+
+def get_available_symbols():
+    """获取固定的交易对列表"""
+    # 返回固定的主要交易对，不再从币安API获取
+    return [
+        {'symbol': 'BTCUSDT', 'baseAsset': 'BTC', 'quoteAsset': 'USDT', 'name': 'BTC/USDT'},
+        {'symbol': 'ETHUSDT', 'baseAsset': 'ETH', 'quoteAsset': 'USDT', 'name': 'ETH/USDT'},
+        {'symbol': 'SOLUSDT', 'baseAsset': 'SOL', 'quoteAsset': 'USDT', 'name': 'SOL/USDT'},
+        {'symbol': 'BNBUSDT', 'baseAsset': 'BNB', 'quoteAsset': 'USDT', 'name': 'BNB/USDT'}
+    ]
+
+
+
+def get_binance_klines(symbol, interval='1h', limit=1000):
+    """从币安获取K线数据，如果失败则生成模拟数据"""
+    try:
+        # 尝试初始化客户端并获取真实的币安数据
+        client = Client("", "")
+        klines = client.get_klines(
+            symbol=symbol,
+            interval=interval,
+            limit=limit
+        )
+
+        # 转换为DataFrame
+        df = pd.DataFrame(klines, columns=[
+            'timestamp', 'open', 'high', 'low', 'close', 'volume',
+            'close_time', 'quote_asset_volume', 'number_of_trades',
+            'taker_buy_base_asset_volume', 'taker_buy_quote_asset_volume', 'ignore'
+        ])
+
+        # 数据类型转换，转换为东八区时间
+        df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms', utc=True)
+        df['timestamp'] = df['timestamp'].dt.tz_convert(BEIJING_TZ)
+        df['timestamps'] = df['timestamp']  # 保持兼容性
+
+        # 转换数值列
+        numeric_cols = ['open', 'high', 'low', 'close', 'volume', 'quote_asset_volume']
+        for col in numeric_cols:
+            df[col] = pd.to_numeric(df[col], errors='coerce')
+
+        # 添加amount列（成交额）
+        df['amount'] = df['quote_asset_volume']
+
+        # 只保留需要的列
+        df = df[['timestamp','timestamps',  'open', 'high', 'low', 'close', 'volume', 'amount']]
+
+        # 按时间排序
+        df = df.sort_values('timestamp').reset_index(drop=True)
+
+        # 添加技术指标（如果可用）
+        if TECHNICAL_INDICATORS_AVAILABLE:
+            try:
+                df = add_technical_indicators(df)
+                print(f"✅ 成功获取币安真实数据并计算技术指标: {symbol} {interval} {len(df)}条，{len(df.columns)}个特征")
+            except Exception as e:
+                print(f"⚠️ 技术指标计算失败，使用原始数据: {e}")
+        else:
+            print(f"✅ 成功获取币安真实数据: {symbol} {interval} {len(df)}条")
+
+        return df, None
+
+    except Exception as e:
+        print(f"⚠️ 币安API连接失败，使用模拟数据: {str(e)}")
+
+
+def get_timeframe_options():
+    """获取可用的时间周期选项"""
+    return [
+        {'value': '1m', 'label': '1分钟', 'description': '1分钟K线'},
+        {'value': '5m', 'label': '5分钟', 'description': '5分钟K线'},
+        {'value': '15m', 'label': '15分钟', 'description': '15分钟K线'},
+        {'value': '30m', 'label': '30分钟', 'description': '30分钟K线'},
+        {'value': '1h', 'label': '1小时', 'description': '1小时K线'},
+        {'value': '4h', 'label': '4小时', 'description': '4小时K线'},
+        {'value': '1d', 'label': '1天', 'description': '日K线'},
+        {'value': '1w', 'label': '1周', 'description': '周K线'},
+    ]
+
+
+def save_prediction_results(file_path, prediction_type, prediction_results, actual_data, input_data, prediction_params):
+    """Save prediction results to file"""
+    try:
+        # Create prediction results directory
+        results_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'prediction_results')
+        os.makedirs(results_dir, exist_ok=True)
+
+        # Generate filename
+        timestamp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
+        filename = f'prediction_{timestamp}.json'
+        filepath = os.path.join(results_dir, filename)
+
+        # Prepare data for saving
+        save_data = {
+            'timestamp': datetime.datetime.now().isoformat(),
+            'file_path': file_path,
+            'prediction_type': prediction_type,
+            'prediction_params': prediction_params,
+            'input_data_summary': {
+                'rows': len(input_data),
+                'columns': list(input_data.columns),
+                'price_range': {
+                    'open': {'min': float(input_data['open'].min()), 'max': float(input_data['open'].max())},
+                    'high': {'min': float(input_data['high'].min()), 'max': float(input_data['high'].max())},
+                    'low': {'min': float(input_data['low'].min()), 'max': float(input_data['low'].max())},
+                    'close': {'min': float(input_data['close'].min()), 'max': float(input_data['close'].max())}
+                },
+                'last_values': {
+                    'open': float(input_data['open'].iloc[-1]),
+                    'high': float(input_data['high'].iloc[-1]),
+                    'low': float(input_data['low'].iloc[-1]),
+                    'close': float(input_data['close'].iloc[-1])
+                }
+            },
+            'prediction_results': prediction_results,
+            'actual_data': actual_data,
+            'analysis': {}
+        }
+
+        # If actual data exists, perform comparison analysis
+        if actual_data and len(actual_data) > 0:
+            # Calculate continuity analysis
+            if len(prediction_results) > 0 and len(actual_data) > 0:
+                last_pred = prediction_results[0]  # First prediction point
+            first_actual = actual_data[0]  # First actual point
+
+            save_data['analysis']['continuity'] = {
+                'last_prediction': {
+                    'open': last_pred['open'],
+                    'high': last_pred['high'],
+                    'low': last_pred['low'],
+                    'close': last_pred['close']
+                },
+                'first_actual': {
+                    'open': first_actual['open'],
+                    'high': first_actual['high'],
+                    'low': first_actual['low'],
+                    'close': first_actual['close']
+                },
+                'gaps': {
+                    'open_gap': abs(last_pred['open'] - first_actual['open']),
+                    'high_gap': abs(last_pred['high'] - first_actual['high']),
+                    'low_gap': abs(last_pred['low'] - first_actual['low']),
+                    'close_gap': abs(last_pred['close'] - first_actual['close'])
+                },
+                'gap_percentages': {
+                    'open_gap_pct': (abs(last_pred['open'] - first_actual['open']) / first_actual['open']) * 100,
+                    'high_gap_pct': (abs(last_pred['high'] - first_actual['high']) / first_actual['high']) * 100,
+                    'low_gap_pct': (abs(last_pred['low'] - first_actual['low']) / first_actual['low']) * 100,
+                    'close_gap_pct': (abs(last_pred['close'] - first_actual['close']) / first_actual['close']) * 100
+                }
+            }
+
+        # Save to file
+        with open(filepath, 'w', encoding='utf-8') as f:
+            json.dump(save_data, f, indent=2, ensure_ascii=False)
+
+        print(f"Prediction results saved to: {filepath}")
+        return filepath
+
+    except Exception as e:
+        print(f"Failed to save prediction results: {e}")
+        return None
+
+
+def create_prediction_chart(df, pred_df, lookback, pred_len, actual_df=None, historical_start_idx=0):
+    """Create prediction chart"""
+    # Use specified historical data start position, not always from the beginning of df
+    if historical_start_idx + lookback + pred_len <= len(df):
+        # Display lookback historical points + pred_len prediction points starting from specified position
+        historical_df = df.iloc[historical_start_idx:historical_start_idx + lookback]
+        prediction_range = range(historical_start_idx + lookback, historical_start_idx + lookback + pred_len)
+    else:
+        # If data is insufficient, adjust to maximum available range
+        available_lookback = min(lookback, len(df) - historical_start_idx)
+        available_pred_len = min(pred_len, max(0, len(df) - historical_start_idx - available_lookback))
+        historical_df = df.iloc[historical_start_idx:historical_start_idx + available_lookback]
+        prediction_range = range(historical_start_idx + available_lookback,
+                                 historical_start_idx + available_lookback + available_pred_len)
+
+    # Create chart
+    fig = go.Figure()
+
+    # Add historical data (candlestick chart)
+    fig.add_trace(go.Candlestick(
+        x=historical_df['timestamps'] if 'timestamps' in historical_df.columns else historical_df.index,
+        open=historical_df['open'],
+        high=historical_df['high'],
+        low=historical_df['low'],
+        close=historical_df['close'],
+        name='Historical Data (400 data points)',
+        increasing_line_color='#26A69A',
+        decreasing_line_color='#EF5350'
+    ))
+
+    # Add prediction data (candlestick chart)
+    if pred_df is not None and len(pred_df) > 0:
+        # Calculate prediction data timestamps - ensure continuity with historical data
+        if 'timestamps' in df.columns and len(historical_df) > 0:
+            # Start from the last timestamp of historical data, create prediction timestamps with the same time interval
+            last_timestamp = historical_df['timestamps'].iloc[-1]
+            time_diff = df['timestamps'].iloc[1] - df['timestamps'].iloc[0] if len(df) > 1 else pd.Timedelta(hours=1)
+
+            pred_timestamps = pd.date_range(
+                start=last_timestamp + time_diff,
+                periods=len(pred_df),
+                freq=time_diff
+            )
+        else:
+            # If no timestamps, use index
+            pred_timestamps = range(len(historical_df), len(historical_df) + len(pred_df))
+
+        fig.add_trace(go.Candlestick(
+            x=pred_timestamps,
+            open=pred_df['open'],
+            high=pred_df['high'],
+            low=pred_df['low'],
+            close=pred_df['close'],
+            name='Prediction Data (120 data points)',
+            increasing_line_color='#66BB6A',
+            decreasing_line_color='#FF7043'
+        ))
+
+    # Add actual data for comparison (if exists)
+    if actual_df is not None and len(actual_df) > 0:
+        # Actual data should be in the same time period as prediction data
+        if 'timestamps' in df.columns:
+            # Actual data should use the same timestamps as prediction data to ensure time alignment
+            if 'pred_timestamps' in locals():
+                actual_timestamps = pred_timestamps
+            else:
+                # If no prediction timestamps, calculate from the last timestamp of historical data
+                if len(historical_df) > 0:
+                    last_timestamp = historical_df['timestamps'].iloc[-1]
+                    time_diff = df['timestamps'].iloc[1] - df['timestamps'].iloc[0] if len(df) > 1 else pd.Timedelta(
+                        hours=1)
+                    actual_timestamps = pd.date_range(
+                        start=last_timestamp + time_diff,
+                        periods=len(actual_df),
+                        freq=time_diff
+                    )
+                else:
+                    actual_timestamps = range(len(historical_df), len(historical_df) + len(actual_df))
+        else:
+            actual_timestamps = range(len(historical_df), len(historical_df) + len(actual_df))
+
+        fig.add_trace(go.Candlestick(
+            x=actual_timestamps,
+            open=actual_df['open'],
+            high=actual_df['high'],
+            low=actual_df['low'],
+            close=actual_df['close'],
+            name='Actual Data (120 data points)',
+            increasing_line_color='#FF9800',
+            decreasing_line_color='#F44336'
+        ))
+
+    # Update layout
+    fig.update_layout(
+        title='Kronos Financial Prediction Results - 400 Historical Points + 120 Prediction Points vs 120 Actual Points',
+        xaxis_title='Time',
+        yaxis_title='Price',
+        template='plotly_white',
+        height=600,
+        showlegend=True
+    )
+
+    # Ensure x-axis time continuity
+    if 'timestamps' in historical_df.columns:
+        # Get all timestamps and sort them
+        all_timestamps = []
+        if len(historical_df) > 0:
+            all_timestamps.extend(historical_df['timestamps'])
+        if 'pred_timestamps' in locals():
+            all_timestamps.extend(pred_timestamps)
+        if 'actual_timestamps' in locals():
+            all_timestamps.extend(actual_timestamps)
+
+        if all_timestamps:
+            all_timestamps = sorted(all_timestamps)
+            fig.update_xaxes(
+                range=[all_timestamps[0], all_timestamps[-1]],
+                rangeslider_visible=False,
+                type='date'
+            )
+
+    return json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
+
+
+@app.route('/')
+def index():
+    """Home page"""
+    return render_template('index.html')
+
+
+@app.route('/api/symbols')
+def get_symbols():
+    """获取可用的交易对列表"""
+    symbols = get_available_symbols()
+    return jsonify(symbols)
+
+
+@app.route('/api/timeframes')
+def get_timeframes():
+    """获取可用的时间周期列表"""
+    timeframes = get_timeframe_options()
+    return jsonify(timeframes)
+
+
+@app.route('/api/technical-indicators')
+def get_technical_indicators():
+    """获取可用的技术指标列表"""
+    indicators = get_available_indicators()
+    return jsonify(indicators)
+
+
+@app.route('/api/load-data', methods=['POST'])
+def load_data():
+    """加载币安数据"""
+    try:
+        data = request.get_json()
+        symbol = data.get('symbol')
+        interval = data.get('interval', '1h')
+        limit = int(data.get('limit', 1000))
+
+        if not symbol:
+            return jsonify({'error': '交易对不能为空'}), 400
+
+        df, error = get_binance_klines(symbol, interval, limit)
+        if error:
+            return jsonify({'error': error}), 400
+
+        # Detect data time frequency
+        def detect_timeframe(df):
+            if len(df) < 2:
+                return "Unknown"
+
+            time_diffs = []
+            for i in range(1, min(10, len(df))):  # Check first 10 time differences
+                diff = df['timestamps'].iloc[i] - df['timestamps'].iloc[i - 1]
+                time_diffs.append(diff)
+
+            if not time_diffs:
+                return "Unknown"
+
+            # Calculate average time difference
+            avg_diff = sum(time_diffs, pd.Timedelta(0)) / len(time_diffs)
+
+            # Convert to readable format
+            if avg_diff < pd.Timedelta(minutes=1):
+                return f"{avg_diff.total_seconds():.0f} seconds"
+            elif avg_diff < pd.Timedelta(hours=1):
+                return f"{avg_diff.total_seconds() / 60:.0f} minutes"
+            elif avg_diff < pd.Timedelta(days=1):
+                return f"{avg_diff.total_seconds() / 3600:.0f} hours"
+            else:
+                return f"{avg_diff.days} days"
+
+        # Return data information with formatted time
+        def format_beijing_time(timestamp):
+            """格式化东八区时间为 yyyy-MM-dd HH:mm:ss"""
+            if pd.isna(timestamp):
+                return 'N/A'
+            # 确保时间戳有时区信息
+            if timestamp.tz is None:
+                timestamp = timestamp.tz_localize(BEIJING_TZ)
+            elif timestamp.tz != BEIJING_TZ:
+                timestamp = timestamp.tz_convert(BEIJING_TZ)
+            return timestamp.strftime('%Y-%m-%d %H:%M:%S')
+
+        data_info = {
+            'rows': len(df),
+            'columns': list(df.columns),
+            'start_date': format_beijing_time(df['timestamps'].min()) if 'timestamps' in df.columns else 'N/A',
+            'end_date': format_beijing_time(df['timestamps'].max()) if 'timestamps' in df.columns else 'N/A',
+            'price_range': {
+                'min': float(df[['open', 'high', 'low', 'close']].min().min()),
+                'max': float(df[['open', 'high', 'low', 'close']].max().max())
+            },
+            'prediction_columns': ['open', 'high', 'low', 'close'] + (['volume'] if 'volume' in df.columns else []),
+            'timeframe': detect_timeframe(df)
+        }
+
+        return jsonify({
+            'success': True,
+            'data_info': data_info,
+            'message': f'Successfully loaded data, total {len(df)} rows'
+        })
+
+    except Exception as e:
+        return jsonify({'error': f'Failed to load data: {str(e)}'}), 500
+
+
+@app.route('/api/predict', methods=['POST'])
+def predict():
+    """Perform prediction"""
+    try:
+        data = request.get_json()
+        symbol = data.get('symbol')
+        interval = data.get('interval', '1h')
+        limit = int(data.get('limit', 1000))
+        lookback = int(data.get('lookback', 400))
+        pred_len = int(data.get('pred_len', 120))
+
+        # Get prediction quality parameters
+        temperature = float(data.get('temperature', 1.0))
+        top_p = float(data.get('top_p', 0.9))
+        sample_count = int(data.get('sample_count', 1))
+
+        if not symbol:
+            return jsonify({'error': '交易对不能为空'}), 400
+
+        # Load data from Binance
+        df, error = get_binance_klines(symbol, interval, limit)
+        if error:
+            return jsonify({'error': error}), 400
+
+        if len(df) < lookback:
+            return jsonify({'error': f'Insufficient data length, need at least {lookback} rows'}), 400
+
+        # Perform prediction
+        if MODEL_AVAILABLE:
+            try:
+                # Use real Kronos model
+                # Only use necessary columns: OHLCVA (6 features required by Kronos model)
+                required_cols = ['open', 'high', 'low', 'close']
+                if 'volume' in df.columns:
+                    required_cols.append('volume')
+                if 'amount' in df.columns:
+                    required_cols.append('amount')
+
+                print(f"🔍 Using features for prediction: {required_cols}")
+                print(f"   Available columns in data: {list(df.columns)}")
+                print(f"   Data shape: {df.shape}")
+
+                # Check if required columns exist
+                missing_cols = [col for col in required_cols if col not in df.columns]
+                if missing_cols:
+                    return jsonify({'error': f'Missing required columns: {missing_cols}'}), 400
+
+                # Process time period selection
+                start_date = data.get('start_date')
+
+                if start_date:
+                    # Custom time period - fix logic: use data within selected window
+                    start_dt = pd.to_datetime(start_date)
+
+                    # Find data after start time
+                    mask = df['timestamps'] >= start_dt
+                    time_range_df = df[mask]
+
+                    # Ensure sufficient data: lookback + pred_len
+                    if len(time_range_df) < lookback + pred_len:
+                        return jsonify({
+                                           'error': f'Insufficient data from start time {start_dt.strftime("%Y-%m-%d %H:%M")}, need at least {lookback + pred_len} data points, currently only {len(time_range_df)} available'}), 400
+
+                    # Use first lookback data points within selected window for prediction
+                    x_df = time_range_df.iloc[:lookback][required_cols]
+                    x_timestamp = time_range_df.iloc[:lookback]['timestamps']
+
+                    print(f"🔍 Custom time period - x_df shape: {x_df.shape}")
+                    print(f"   x_timestamp length: {len(x_timestamp)}")
+                    print(f"   x_df columns: {list(x_df.columns)}")
+                    print(f"   x_df sample:\n{x_df.head()}")
+
+                    # Generate future timestamps for prediction instead of using existing data
+                    # Calculate time difference from the data
+                    if len(time_range_df) >= 2:
+                        time_diff = time_range_df['timestamps'].iloc[1] - time_range_df['timestamps'].iloc[0]
+                    else:
+                        time_diff = pd.Timedelta(hours=1)  # Default to 1 hour
+                    
+                    # Generate future timestamps starting from the last timestamp of input data
+                    last_timestamp = time_range_df['timestamps'].iloc[lookback - 1]
+                    y_timestamp = pd.date_range(
+                        start=last_timestamp + time_diff,
+                        periods=pred_len,
+                        freq=time_diff
+                    )
+
+                    # Calculate actual time period length
+                    start_timestamp = time_range_df['timestamps'].iloc[0]
+                    end_timestamp = y_timestamp[-1]  # Use the last generated timestamp
+                    time_span = end_timestamp - start_timestamp
+
+                    prediction_type = f"Kronos model prediction (within selected window: first {lookback} data points for prediction, {pred_len} future predictions, time span: {time_span})"
+                else:
+                    # Use latest data
+                    x_df = df.iloc[:lookback][required_cols]
+                    x_timestamp = df.iloc[:lookback]['timestamps']
+                    
+                    # Generate future timestamps for prediction instead of using existing data
+                    # Calculate time difference from the data
+                    if len(df) >= 2:
+                        time_diff = df['timestamps'].iloc[1] - df['timestamps'].iloc[0]
+                    else:
+                        time_diff = pd.Timedelta(hours=1)  # Default to 1 hour
+                    
+                    # Generate future timestamps starting from the last timestamp of input data
+                    last_timestamp = df['timestamps'].iloc[lookback - 1]
+                    y_timestamp = pd.date_range(
+                        start=last_timestamp + time_diff,
+                        periods=pred_len,
+                        freq=time_diff
+                    )
+                    prediction_type = "Kronos model prediction (latest data)"
+
+                    print(f"🔍 Latest data - x_df shape: {x_df.shape}")
+                    print(f"   x_timestamp length: {len(x_timestamp)}")
+                    print(f"   y_timestamp length: {len(y_timestamp)}")
+                    print(f"   x_df columns: {list(x_df.columns)}")
+                    print(f"   x_df sample:\n{x_df.head()}")
+
+                # Check if data is empty
+                if x_df.empty or len(x_df) == 0:
+                    return jsonify({'error': 'Input data is empty after processing'}), 400
+
+                if len(x_timestamp) == 0:
+                    return jsonify({'error': 'Input timestamps are empty'}), 400
+
+                if len(y_timestamp) == 0:
+                    return jsonify({'error': 'Target timestamps are empty'}), 400
+
+                # Ensure timestamps are Series format, not DatetimeIndex, to avoid .dt attribute error in Kronos model
+                if isinstance(x_timestamp, pd.DatetimeIndex):
+                    x_timestamp = pd.Series(x_timestamp, name='timestamps')
+                if isinstance(y_timestamp, pd.DatetimeIndex):
+                    y_timestamp = pd.Series(y_timestamp, name='timestamps')
+
+                pred_df = predictor.predict(
+                    df=x_df,
+                    x_timestamp=x_timestamp,
+                    y_timestamp=y_timestamp,
+                    pred_len=pred_len,
+                    T=temperature,
+                    top_p=top_p,
+                    sample_count=sample_count
+                )
+
+            except Exception as e:
+                return jsonify({'error': f'Kronos model prediction failed: {str(e)}'}), 500
+        else:
+            return jsonify({'error': 'Kronos model not loaded, please load model first'}), 400
+
+        # Prepare actual data for comparison (if exists)
+        actual_data = []
+        actual_df = None
+
+        if start_date:  # Custom time period
+            # Fix logic: use data within selected window
+            # Prediction uses first 400 data points within selected window
+            # Actual data should be last 120 data points within selected window
+            start_dt = pd.to_datetime(start_date)
+            # 确保时区一致性
+            if start_dt.tz is None:
+                start_dt = start_dt.tz_localize(BEIJING_TZ)
+
+            # Find data starting from start_date
+            mask = df['timestamps'] >= start_dt
+            time_range_df = df[mask]
+
+            if len(time_range_df) >= lookback + pred_len:
+                # Get last 120 data points within selected window as actual values
+                actual_df = time_range_df.iloc[lookback:lookback + pred_len]
+
+                for i, (_, row) in enumerate(actual_df.iterrows()):
+                    actual_data.append({
+                        'timestamp': row['timestamps'].isoformat(),
+                        'open': float(row['open']),
+                        'high': float(row['high']),
+                        'low': float(row['low']),
+                        'close': float(row['close']),
+                        'volume': float(row['volume']) if 'volume' in row else 0,
+                        'amount': float(row['amount']) if 'amount' in row else 0
+                    })
+        else:  # Latest data
+            # Prediction uses first 400 data points
+            # Actual data should be 120 data points after first 400 data points
+            if len(df) >= lookback + pred_len:
+                actual_df = df.iloc[lookback:lookback + pred_len]
+                for i, (_, row) in enumerate(actual_df.iterrows()):
+                    actual_data.append({
+                        'timestamp': row['timestamps'].isoformat(),
+                        'open': float(row['open']),
+                        'high': float(row['high']),
+                        'low': float(row['low']),
+                        'close': float(row['close']),
+                        'volume': float(row['volume']) if 'volume' in row else 0,
+                        'amount': float(row['amount']) if 'amount' in row else 0
+                    })
+
+        # Create chart - pass historical data start position
+        if start_date:
+            # Custom time period: find starting position of historical data in original df
+            start_dt = pd.to_datetime(start_date)
+            # 确保时区一致性
+            if start_dt.tz is None:
+                start_dt = start_dt.tz_localize(BEIJING_TZ)
+            mask = df['timestamps'] >= start_dt
+            historical_start_idx = df[mask].index[0] if len(df[mask]) > 0 else 0
+        else:
+            # Latest data: start from beginning
+            historical_start_idx = 0
+
+        chart_json = create_prediction_chart(df, pred_df, lookback, pred_len, actual_df, historical_start_idx)
+
+        # Prepare prediction result data - fix timestamp calculation logic
+        if 'timestamps' in df.columns:
+            if start_date:
+                # Custom time period: use selected window data to calculate timestamps
+                start_dt = pd.to_datetime(start_date)
+                # 确保时区一致性
+                if start_dt.tz is None:
+                    start_dt = start_dt.tz_localize(BEIJING_TZ)
+                mask = df['timestamps'] >= start_dt
+                time_range_df = df[mask]
+
+                if len(time_range_df) >= lookback:
+                    # Calculate prediction timestamps starting from last time point of selected window
+                    last_timestamp = time_range_df['timestamps'].iloc[lookback - 1]
+                    time_diff = df['timestamps'].iloc[1] - df['timestamps'].iloc[0]
+                    future_timestamps = pd.date_range(
+                        start=last_timestamp + time_diff,
+                        periods=pred_len,
+                        freq=time_diff
+                    )
+                else:
+                    future_timestamps = []
+            else:
+                # Latest data: calculate from last time point of entire data file
+                last_timestamp = df['timestamps'].iloc[-1]
+                time_diff = df['timestamps'].iloc[1] - df['timestamps'].iloc[0]
+                future_timestamps = pd.date_range(
+                    start=last_timestamp + time_diff,
+                    periods=pred_len,
+                    freq=time_diff
+                )
+        else:
+            future_timestamps = range(len(df), len(df) + pred_len)
+
+        prediction_results = []
+        for i, (_, row) in enumerate(pred_df.iterrows()):
+            prediction_results.append({
+                'timestamp': future_timestamps[i].isoformat() if i < len(future_timestamps) else f"T{i}",
+                'open': float(row['open']),
+                'high': float(row['high']),
+                'low': float(row['low']),
+                'close': float(row['close']),
+                'volume': float(row['volume']) if 'volume' in row else 0,
+                'amount': float(row['amount']) if 'amount' in row else 0
+            })
+
+        # Save prediction results to file
+        try:
+            data_source = f"{symbol}_{interval}"
+            save_prediction_results(
+                file_path=data_source,
+                prediction_type=prediction_type,
+                prediction_results=prediction_results,
+                actual_data=actual_data,
+                input_data=x_df,
+                prediction_params={
+                    'symbol': symbol,
+                    'interval': interval,
+                    'limit': limit,
+                    'lookback': lookback,
+                    'pred_len': pred_len,
+                    'temperature': temperature,
+                    'top_p': top_p,
+                    'sample_count': sample_count,
+                    'start_date': start_date if start_date else 'latest'
+                }
+            )
+        except Exception as e:
+            print(f"Failed to save prediction results: {e}")
+
+        return jsonify({
+            'success': True,
+            'prediction_type': prediction_type,
+            'chart': chart_json,
+            'prediction_results': prediction_results,
+            'actual_data': actual_data,
+            'has_comparison': len(actual_data) > 0,
+            'message': f'Prediction completed, generated {pred_len} prediction points' + (
+                f', including {len(actual_data)} actual data points for comparison' if len(actual_data) > 0 else '')
+        })
+
+    except Exception as e:
+        return jsonify({'error': f'Prediction failed: {str(e)}'}), 500
+
+
+@app.route('/api/load-model', methods=['POST'])
+def load_model():
+    """Load Kronos model"""
+    global tokenizer, model, predictor
+
+    try:
+        if not MODEL_AVAILABLE:
+            return jsonify({'error': 'Kronos model library not available'}), 400
+
+        data = request.get_json()
+        model_key = data.get('model_key', 'kronos-small')
+        device = data.get('device', 'cpu')
+
+        if model_key not in AVAILABLE_MODELS:
+            return jsonify({'error': f'Unsupported model: {model_key}'}), 400
+
+        model_config = AVAILABLE_MODELS[model_key]
+
+        # Load tokenizer and model
+        tokenizer = KronosTokenizer.from_pretrained(model_config['tokenizer_id'])
+        model = Kronos.from_pretrained(model_config['model_id'])
+
+        # Create predictor
+        predictor = KronosPredictor(model, tokenizer, device=device, max_context=model_config['context_length'])
+
+        return jsonify({
+            'success': True,
+            'message': f'Model loaded successfully: {model_config["name"]} ({model_config["params"]}) on {device}',
+            'model_info': {
+                'name': model_config['name'],
+                'params': model_config['params'],
+                'context_length': model_config['context_length'],
+                'description': model_config['description']
+            }
+        })
+
+    except Exception as e:
+        return jsonify({'error': f'Model loading failed: {str(e)}'}), 500
+
+
+@app.route('/api/available-models')
+def get_available_models():
+    """Get available model list"""
+    return jsonify({
+        'models': AVAILABLE_MODELS,
+        'model_available': MODEL_AVAILABLE
+    })
+
+
+@app.route('/api/model-status')
+def get_model_status():
+    """Get model status"""
+    if MODEL_AVAILABLE:
+        if predictor is not None:
+            return jsonify({
+                'available': True,
+                'loaded': True,
+                'message': 'Kronos model loaded and available',
+                'current_model': {
+                    'name': predictor.model.__class__.__name__,
+                    'device': str(next(predictor.model.parameters()).device)
+                }
+            })
+        else:
+            return jsonify({
+                'available': True,
+                'loaded': False,
+                'message': 'Kronos model available but not loaded'
+            })
+    else:
+        return jsonify({
+            'available': False,
+            'loaded': False,
+            'message': 'Kronos model library not available, please install related dependencies'
+        })
+
+
+if __name__ == '__main__':
+    print("Starting Kronos Web UI...")
+    print(f"Model availability: {MODEL_AVAILABLE}")
+    if MODEL_AVAILABLE:
+        print("Tip: You can load Kronos model through /api/load-model endpoint")
+    else:
+        print("Tip: Will use simulated data for demonstration")
+
+    app.run(debug=True, host='0.0.0.0', port=7070)